Confiscation Detection of Criminal Judgment Using Text Classification Approach

1041

Authors

nan

Abstract

AbstractAs the system of confiscation becomes more and more perfect, grasping the distribution of the types of confiscations actually announced by the courts will enable you to understand changing of the trend. In addition to assisting legislators in formulating laws, it can also provide other people with an understanding of the actual operation of the confiscation system. In order to enable artificial intelligence technology to automatically identify the distribution of confiscation, and consumes a lot of manpower and time costs of manual judgment. The purpose of this research is to establish an automated confiscation identification model that can quickly and accurately identify the multiple label categories of confiscation, and provide the needs of all social circles for confiscation information, so as to facilitate subsequent law amendments or discretion. This research uses the first instance criminal cases as the main experimental data. According to the current laws, the confiscation is divided into three categories: contrabands, criminal tools and criminal proceeds, and perform multiple label identification. This research will use Term Frequency–Inverse Document Frequency (TF-IDF) and Word2Vec algorithm as the feature extraction algorithm, with random forest classifier, and CKIPlabBERT pretrained model for training and identification. The experimental results show that under the CKIPlabBERT pretrained model, the best identification effect can be obtained when only use sentences with confiscated words mentioned in the judgment. When the task is case confiscation, the Micro F1 Score can be as high as 96.2716%, and when the task is defendant confiscation, the Micro F1 Score is as high as 95.5478%.

Keywords

nan

Full Content

�� Term Frequency�Inverse Document Frequency �TF-IDF�� Word2Vec �� CKIPlabBERT �� CKIPlabBERT �� Micro F1 �� 96.2716%�� Micro F1 �� 95.5478%� Abstract As the system of confiscation becomes more and more perfect, grasping the distribution of the types of confiscations actually announced by the courts will enable you to understand changing of the trend. In addition to assisting legislators in formulating laws, it can also provide other people with an understanding of the actual operation of the confiscation system. In order to enable artificial intelligence technology to automatically identify the distribution of confiscation, and consumes a lot of manpower and time costs of manual judgment. The purpose of this research is to establish an automated confiscation identification model that can quickly and accurately identify the multiple label categories of confiscation, and provide the needs of all social circles for confiscation information, so as to facilitate subsequent law amendments or discretion. This research uses the first instance criminal cases as the main experimental data. According to the current laws, the confiscation is divided into three categories: contrabands, criminal tools and criminal proceeds, and perform multiple label identification. This research will use Term Frequency�Inverse Document Frequency (TF-IDF) and Word2Vec algorithm as the feature extraction algorithm, with random forest classifier, and CKIPlabBERT pretrained model for training and identification. The experimental results show that under the CKIPlabBERT pretrained model, the best identification effect can be obtained when only use sentences with confiscated words mentioned in the judgment. When the task is case confiscation, the Micro F1 Score can be as high as 96.2716%, and when the task is defendant confiscation, the Micro F1 Score is as high as 95.5478%. �� Keywords � Criminal Cases, Confiscation, Text Mining, Machine Learning, Pretrained model, Multi- label Detection 1 �� 105 �� 12 �� 23 �� Confiscation Detection of Criminal Judgment Using Text Classification Approach �� Hsuan-Tzu Shih School of Big Data Management, Soochow University, Taiwan dollyshih39@gmail.com �� Hsiao-Shih Chen School of Big Data Management, Soochow University, Taiwan kellywithchen@ gmail.com �� Yu-Cheng Chiu School of Big Data Management, Soochow University, Taiwan jean199925@gmail.com �� Jheng-Long Wu Department of Data Science, Soochow University, Taiwan jlwu@gm.scu.edu.tw The 33rd Conference on Computational Linguistics and Speech Processing (ROCLING 2021) Taoyuan, Taiwan, October 15-16, 2021. The Association for Computational Linguistics and Chinese Language Processing 318 ��108 �� 5,676 �� 99 �� 104 �� 3.1 � ��105 �� 105 � 108 �� 4.3 �� 38 �� 38 � � 1 �� BERT �� BERT �� 1�� 2�� 2 � �� 2.1 �� 105 � 7 �� 38 �� 2014�� …

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Publication Details

Paper ID:: 1041
Year:: 2021
Publication:: TIPSTER
Publication Type:: conf
DOI:: nan

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